Autonomous driving is self‐driving without the intervention of a human driver. A self‐driving autonomous vehicle is designed with the help of high‐technology sensors that can sense the traffic and traffic signals in the surroundings and move accordingly. It becomes necessary for a self‐driving vehicle to take a right decision at the right time in an uncertain traffic environment. Any unusual anomalous activity or unexpected obstacle that could not be detected by an autonomous vehicle can lead to a road accident. For decision making in autonomous vehicles, very precisely designed and optimized programming software are developed and intensively trained to install in vehicle's computer system. But in spite of these trained software some of the anomalous activity could become a hindrance to detect promptly during self‐driving. Therefore, automatic detection and recognition of anomalies in autonomous vehicles is critical to a safe drive. In this chapter we discuss and propos deep learning method for anonymous activity detection of other vehicles that can be danger for safe driving in an autonomous vehicle. The present chapter focuses on various conditions and possible anomalies that should be known to handle while developing software for autonomous vehicles using deep learning models. A variety of deep learning models were tested to detect abnormalities, and we discovered that deep learning models can detect anomalies in real time. We have also observed that incremental development in YOLO (You Only Look Once) make it more accurate and agile in object detection. We suggest that anomalies should be detected in real time and YOLO can play a vital role in anomalous activity.


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    Title :

    Anomalous Activity Detection Using Deep Learning Techniques in Autonomous Vehicles


    Contributors:

    Published in:

    Publication date :

    2022-12-19


    Size :

    25 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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